2e0b4a8358
- 在 PostgresManager 中添加了用于嵌入和重新排序基本 URL 的新列。 - 更新了模型提供程序数据结构，以包含新的端点。 - 重构了模型提供程序服务和路由器中的凭据状态检查。 - 引入了一个可重用的组件，用于跨组件进行模型状态检查。 - 通过调整页面内边距的 CSS 变量，提高了 UI 的响应速度。 - 重构了模型选择器组件，以利用新的模型状态组件。 - 更新了模型配置视图，以处理响应式编辑模型状态。 - 清理了未使用的代码，并改进了模型状态检查中的错误处理。
292 lines
10 KiB
Python
292 lines
10 KiB
Python
import os
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import pytest
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os.environ.setdefault("OPENAI_API_KEY", "test-key")
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from yuxi.config.builtin_providers import BUILTIN_PROVIDERS
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from yuxi.services.model_provider_service import (
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check_credential_status,
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_normalize_payload,
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_normalize_remote_model,
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fetch_remote_models,
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)
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def test_normalize_payload_accepts_enabled_chat_model():
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payload = _normalize_payload(
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{
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"provider_id": "openrouter-local",
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"display_name": "OpenRouter Local",
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"base_url": "https://openrouter.ai/api/v1",
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"enabled_models": [{"id": "anthropic/claude-sonnet-4.5", "type": "chat"}],
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}
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)
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assert payload["provider_id"] == "openrouter-local"
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assert payload["provider_type"] == "openai"
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assert "models_endpoint" not in payload
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assert "embedding_models_endpoint" not in payload
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assert payload["enabled_models"][0]["display_name"] == "anthropic/claude-sonnet-4.5"
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def test_normalize_payload_rejects_unknown_enabled_model_type():
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with pytest.raises(ValueError, match="type 必须是"):
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_normalize_payload(
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{
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"provider_id": "openrouter-local",
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"display_name": "OpenRouter Local",
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"base_url": "https://openrouter.ai/api/v1",
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"enabled_models": [{"id": "unknown-model", "type": "unknown"}],
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}
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)
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def test_normalize_payload_allows_embedding_without_dimension():
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"""embedding 模型的 dimension 是可选字段，不提供也不会报错。"""
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payload = _normalize_payload(
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{
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"provider_id": "embedding-local",
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"display_name": "Embedding Local",
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"base_url": "https://example.com/v1",
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"capabilities": ["embedding"],
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"embedding_base_url": "https://example.com/v1/embeddings",
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"enabled_models": [{"id": "text-embedding", "type": "embedding"}],
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}
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)
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assert payload["provider_id"] == "embedding-local"
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assert payload["enabled_models"][0].get("dimension") is None
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def test_normalize_remote_model_preserves_detailed_model_config():
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model = _normalize_remote_model(
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{
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"id": "xiaomi/mimo-v2-omni",
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"name": "Xiaomi: MiMo-V2-Omni",
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"context_length": 262144,
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"architecture": {
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"input_modalities": ["text", "audio", "image", "video"],
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"output_modalities": ["text"],
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},
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"top_provider": {"max_completion_tokens": 65536},
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"supported_parameters": ["temperature", "tools"],
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}
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)
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assert model["id"] == "xiaomi/mimo-v2-omni"
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assert model["display_name"] == "Xiaomi: MiMo-V2-Omni"
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assert model["type"] == "chat"
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assert model["input_modalities"] == ["text", "audio", "image", "video"]
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assert model["max_completion_tokens"] == 65536
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assert model["raw_metadata"]["supported_parameters"] == ["temperature", "tools"]
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def test_normalize_remote_model_uses_endpoint_model_type():
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model = _normalize_remote_model({"id": "BAAI/bge-m3", "object": "model"}, "embedding")
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assert model["id"] == "BAAI/bge-m3"
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assert model["type"] == "embedding"
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@pytest.mark.asyncio
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async def test_fetch_remote_models_loads_embedding_only_when_capability_enabled(monkeypatch):
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calls = []
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async def fake_fetch(client, provider, headers, endpoint, model_type):
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calls.append((endpoint, model_type))
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return [{"id": f"{model_type}-model", "type": model_type}]
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monkeypatch.setattr("yuxi.services.model_provider_service._fetch_models_from_endpoint", fake_fetch)
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class Provider:
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base_url = "https://example.com/v1"
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api_key = None
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api_key_env = None
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headers_json = {}
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capabilities = ["chat", "embedding", "rerank"]
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models_endpoint = "/models"
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embedding_models_endpoint = "/embeddings/models"
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rerank_models_endpoint = None
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models = await fetch_remote_models(Provider())
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assert calls == [("/models", "chat"), ("/embeddings/models", "embedding")]
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assert [model["type"] for model in models] == ["chat", "embedding"]
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def test_builtin_provider_templates_default_to_openai_provider_type():
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assert len(BUILTIN_PROVIDERS) >= 16
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provider_types = {
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_normalize_payload(
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{
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"provider_id": provider["provider_id"],
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"display_name": provider["display_name"],
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"base_url": provider["base_url"],
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"provider_type": provider.get("provider_type"),
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}
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)["provider_type"]
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for provider in BUILTIN_PROVIDERS
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}
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assert provider_types == {"openai"}
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def test_builtin_siliconflow_provider_includes_default_runnable_models():
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provider = next(item for item in BUILTIN_PROVIDERS if item["provider_id"] == "siliconflow-cn")
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models = {model["id"]: model for model in provider["enabled_models"]}
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assert provider["capabilities"] == ["chat", "embedding", "rerank"]
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assert provider["embedding_base_url"] == "https://api.siliconflow.cn/v1/embeddings"
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assert provider["rerank_base_url"] == "https://api.siliconflow.cn/v1/rerank"
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assert models["Pro/BAAI/bge-m3"]["type"] == "embedding"
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assert models["Pro/BAAI/bge-m3"]["dimension"] == 1024
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assert "base_url_override" not in models["Pro/BAAI/bge-m3"]
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assert models["Pro/BAAI/bge-reranker-v2-m3"]["type"] == "rerank"
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assert "base_url_override" not in models["Pro/BAAI/bge-reranker-v2-m3"]
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def testcheck_credential_status_disabled_provider_always_ok():
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"""未启用的 provider 无论凭证如何配置，状态始终为 ok。"""
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class Provider:
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is_enabled = False
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api_key = None
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api_key_env = None
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assert check_credential_status(Provider()) == "ok"
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def testcheck_credential_status_direct_api_key_ok():
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"""直接配置了 api_key 的启用 provider 状态为 ok。"""
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class Provider:
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is_enabled = True
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api_key = "sk-test"
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api_key_env = None
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assert check_credential_status(Provider()) == "ok"
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def testcheck_credential_status_env_key_exists_ok(monkeypatch):
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"""api_key_env 对应的环境变量存在时状态为 ok。"""
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monkeypatch.setenv("TEST_API_KEY", "exists")
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class Provider:
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is_enabled = True
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api_key = None
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api_key_env = "TEST_API_KEY"
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assert check_credential_status(Provider()) == "ok"
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def testcheck_credential_status_env_key_missing_warning(monkeypatch):
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"""api_key_env 对应的环境变量不存在时状态为 warning。"""
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monkeypatch.delenv("MISSING_KEY", raising=False)
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class Provider:
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is_enabled = True
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api_key = None
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api_key_env = "MISSING_KEY"
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assert check_credential_status(Provider()) == "warning"
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def testcheck_credential_status_both_empty_warning():
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"""api_key 和 api_key_env 都未配置时状态为 warning。"""
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class Provider:
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is_enabled = True
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api_key = None
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api_key_env = None
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assert check_credential_status(Provider()) == "warning"
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# ==================== 手动添加模型 / source 字段 ====================
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def test_normalize_payload_default_model_source_is_remote():
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"""未显式指定 source 时，规范化后默认填入 remote，向后兼容旧数据。"""
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payload = _normalize_payload(
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{
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"provider_id": "openrouter-local",
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"display_name": "OpenRouter Local",
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"base_url": "https://openrouter.ai/api/v1",
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"enabled_models": [{"id": "anthropic/claude-sonnet-4.5", "type": "chat"}],
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}
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)
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assert payload["enabled_models"][0]["source"] == "remote"
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def test_normalize_payload_accepts_manual_source():
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"""source=manual 表示管理员手动添加的模型，规范化保留该标签。"""
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payload = _normalize_payload(
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{
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"provider_id": "custom-local",
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"display_name": "Custom Local",
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"base_url": "https://example.com/v1",
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"capabilities": ["chat"],
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"enabled_models": [
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{"id": "my-chat-model", "type": "chat", "source": "manual"}
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],
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}
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)
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assert payload["enabled_models"][0]["source"] == "manual"
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def test_normalize_payload_rejects_invalid_source():
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"""source 仅允许 manual 或 remote，其他取值视为非法。"""
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with pytest.raises(ValueError, match="source 必须是"):
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_normalize_payload(
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{
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"provider_id": "custom-local",
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"display_name": "Custom Local",
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"base_url": "https://example.com/v1",
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"enabled_models": [
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{"id": "x", "type": "chat", "source": "custom"}
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],
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}
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)
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def test_normalize_payload_rejects_model_type_not_in_capabilities():
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"""provider 仅声明 chat 能力时，不允许写入 embedding 类型的模型。"""
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with pytest.raises(ValueError, match="不在 provider 能力"):
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_normalize_payload(
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{
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"provider_id": "chat-only",
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"display_name": "Chat Only",
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"base_url": "https://example.com/v1",
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"capabilities": ["chat"],
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"enabled_models": [
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{"id": "rogue-embedding", "type": "embedding", "dimension": 1024}
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],
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}
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)
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def test_normalize_payload_allows_model_type_within_capabilities():
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"""provider 同时声明 chat + embedding 时，两类模型均可正常写入。"""
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payload = _normalize_payload(
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{
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"provider_id": "multi-cap",
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"display_name": "Multi Cap",
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"base_url": "https://example.com/v1",
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"capabilities": ["chat", "embedding"],
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"embedding_base_url": "https://example.com/v1/embeddings",
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"embedding_models_endpoint": "/embeddings/models",
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"enabled_models": [
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{"id": "chat-1", "type": "chat", "source": "manual"},
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{
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"id": "embed-1",
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"type": "embedding",
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"source": "manual",
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"dimension": 1024,
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},
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],
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}
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)
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types = [model["type"] for model in payload["enabled_models"]]
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sources = [model["source"] for model in payload["enabled_models"]]
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assert types == ["chat", "embedding"]
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assert sources == ["manual", "manual"]
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